UM
Intelligent fault diagnosis of rolling element bearing based on convolutional neural network and frequency spectrograms
Liang,Pengfei1; Deng,Chao1; Wu,Jun2; Yang,Zhixin3; Zhu,Jinxuan1
2019-06-01
Source Publication2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
AbstractEffective fault diagnosis of rolling element bearing is vital for the reliability and safety of modern industry. Although traditional intelligent fault diagnosis technology such as support vector machine, extreme learning machines and artificial neural network might achieve satisfactory accuracy, expert knowledge and manual intervention are heavily relied on in the process of feature extraction and selection. In this paper, a novel fault diagnosis method based on deep learning is proposed for rolling bearing using convolutional neural networks (CNN) and frequency spectrograms. First of all, fast Fourier transform is used to extract frequency features from raw 1-D vibration signals and convert them into 2-D frequency spectrograms. Then, the extracted 2-D frequency spectrograms are inputted into the CNN model to achieve fault diagnosis of rolling bearing, which makes full use of the strong ability of CNN in image classification. Finally, a case study is carried out to demonstrate the proposed method. The results show that it can achieve higher accuracy than traditional methods. Moreover, its performance in stability is very good as well.
KeywordConvolutional neural networks Fault diagnosis Frequency spectrograms Rolling bearing
DOI10.1109/ICPHM.2019.8819444
URLView the original
Language英语
Scopus ID2-s2.0-85072781529
Fulltext Access
Citation statistics
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.School of Mechanical Science and Engineering,Huazhong University of Science and Technology,Wuhan,China
2.School of Naval Architecture and Ocean Engineering,Huazhong University of Science and Technology,Wuhan,China
3.Department of Electromechanical Engineering,University of Macau,Macao
Recommended Citation
GB/T 7714
Liang,Pengfei,Deng,Chao,Wu,Jun,et al. Intelligent fault diagnosis of rolling element bearing based on convolutional neural network and frequency spectrograms[C],2019.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liang,Pengfei]'s Articles
[Deng,Chao]'s Articles
[Wu,Jun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liang,Pengfei]'s Articles
[Deng,Chao]'s Articles
[Wu,Jun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liang,Pengfei]'s Articles
[Deng,Chao]'s Articles
[Wu,Jun]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.